P3‐221: The MoCA‐S test in low‐education populations: Colombia experience
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is used to help differentiate normal cognitive aging from Mild Cognitive Impairment and dementia, however, the effects of years of education are only minimally taken into consideration. We have investigated the use of the Spanish version (MoCA-S) in a population with little or no formal education in the Andes Mountains of Colombia, to assess the impact of education on scores. The population was screened for dementia using the Leganes Cognitive Test (LCT) developed for populations with none/few years of education. One hundred and fifty community-dwelling subjects aged 65 to 74 years old were recruited at elderly community social centers. They had a mean of 4.8 (SD ± 3.5) years of education. The MoCA-S and the LCT were administered by trained interviewers. The association between the MoCA-S total score and its sub-scores by cognitive domain, and the years of education were examined by ANOVA in the 150 subjects and in the “free of dementia” subjects (n=126) group. Twenty-four persons (16%) tested positive on LCT screening for dementia. MoCA-S scores varied significantly depending on education in the whole sample and in those without dementia. In the whole population, including those who screened positive for dementia, the mean MoCA score was 17.61 (±4.9). By educational achievement, MoCA scores were 16.16 (±4.34) for those without completing primary school or illiterate (n=74), 18.28 (±4.53) among those with primary school (n=46), and 20.13 (±5.82) among those with more than primary school (n=30). There were significant differences by education in the total score (P<0.001) and the subscores for executive function-visuospatial tasks (P<0.008), language fluency (P<0.007), verbal abstraction (P<0.001), and serial substraction (P<0.001). Similar results were found in the “no-dementia” group. Orientation, delayed recall and language (repeat sentences) were less influenced by education. Scores from the MoCA-S test are strongly dependent on education achievement among elderly Colombians with no or few years of education. Very low educational achievement and illiteracy must be considered when interpreting the MoCA scores given the high error rate on items that depend on culture and the ability to read and write.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".